Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths

This article presents an adaptive iterative learning fault-tolerant control algorithm for state constrained nonlinear systems with randomly varying iteration lengths subjected to actuator faults. First, the modified parameters updating laws are designed through a new defined tracking error to handle the randomly varying iteration lengths. Second, the radial basis function neural network method is used to deal with the time-iteration-dependent unknown nonlinearity, and a barrier Lyapunov function is given to cope with the state constraint. Finally, a new barrier composite energy function is used to achieve the tracking error convergence of the presented control algorithm along the iteration axis with the state constraint and then followed with the extension to the high-order case. A simulation for a single-link manipulator is given to illustrate the effectiveness of the theoretical studies.